Overview

Dataset statistics

Number of variables27
Number of observations18
Missing cells58
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory223.1 B

Variable types

Categorical3
Numeric24

Alerts

number_of_countries is highly correlated with aminoglycosides_tonnes and 21 other fieldsHigh correlation
aminoglycosides_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
amphenicols_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
arsenicals_tonnes is highly correlated with number_of_countries and 12 other fieldsHigh correlation
cephalosporins__all_generations_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
1_2_gen__cephalosporins_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
3_4_gen_cephalosporins_tonnes is highly correlated with number_of_countries and 18 other fieldsHigh correlation
fluoroquinolones_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
glycopeptides_tonnes is highly correlated with nitrofurans_tonnesHigh correlation
glycophospholipids_tonnes is highly correlated with number_of_countries and 9 other fieldsHigh correlation
lincosamides_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
macrolides_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
nitrofurans_tonnes is highly correlated with number_of_countries and 13 other fieldsHigh correlation
orthosomycins_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
other_quinolones_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
penicillins_tonnes is highly correlated with number_of_countries and 20 other fieldsHigh correlation
pleuromutilins_tonnes is highly correlated with number_of_countries and 20 other fieldsHigh correlation
polypeptides_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
quinoxalines_tonnes is highly correlated with arsenicals_tonnes and 12 other fieldsHigh correlation
streptogramins_tonnes is highly correlated with number_of_countries and 13 other fieldsHigh correlation
sulfonamides__including_trimethoprim_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
tetracyclines_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
others_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
aggregated_class_data_tonnes is highly correlated with number_of_countries and 19 other fieldsHigh correlation
total_antimicrobials_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
number_of_countries is highly correlated with aminoglycosides_tonnes and 21 other fieldsHigh correlation
aminoglycosides_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
amphenicols_tonnes is highly correlated with number_of_countries and 20 other fieldsHigh correlation
arsenicals_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
cephalosporins__all_generations_tonnes is highly correlated with number_of_countries and 20 other fieldsHigh correlation
1_2_gen__cephalosporins_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
3_4_gen_cephalosporins_tonnes is highly correlated with number_of_countries and 19 other fieldsHigh correlation
fluoroquinolones_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
glycopeptides_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
glycophospholipids_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
lincosamides_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
macrolides_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
nitrofurans_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
orthosomycins_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
other_quinolones_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
penicillins_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
pleuromutilins_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
polypeptides_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
quinoxalines_tonnes is highly correlated with arsenicals_tonnes and 9 other fieldsHigh correlation
streptogramins_tonnes is highly correlated with arsenicals_tonnes and 9 other fieldsHigh correlation
sulfonamides__including_trimethoprim_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
tetracyclines_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
others_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
aggregated_class_data_tonnes is highly correlated with number_of_countries and 20 other fieldsHigh correlation
total_antimicrobials_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
number_of_countries is highly correlated with aminoglycosides_tonnes and 15 other fieldsHigh correlation
aminoglycosides_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
amphenicols_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
arsenicals_tonnes is highly correlated with glycophospholipids_tonnes and 8 other fieldsHigh correlation
cephalosporins__all_generations_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
1_2_gen__cephalosporins_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
3_4_gen_cephalosporins_tonnes is highly correlated with number_of_countries and 17 other fieldsHigh correlation
fluoroquinolones_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
glycophospholipids_tonnes is highly correlated with arsenicals_tonnes and 4 other fieldsHigh correlation
lincosamides_tonnes is highly correlated with number_of_countries and 18 other fieldsHigh correlation
macrolides_tonnes is highly correlated with number_of_countries and 20 other fieldsHigh correlation
nitrofurans_tonnes is highly correlated with number_of_countries and 10 other fieldsHigh correlation
orthosomycins_tonnes is highly correlated with arsenicals_tonnes and 10 other fieldsHigh correlation
other_quinolones_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
penicillins_tonnes is highly correlated with number_of_countries and 15 other fieldsHigh correlation
pleuromutilins_tonnes is highly correlated with number_of_countries and 19 other fieldsHigh correlation
polypeptides_tonnes is highly correlated with number_of_countries and 14 other fieldsHigh correlation
quinoxalines_tonnes is highly correlated with arsenicals_tonnes and 6 other fieldsHigh correlation
streptogramins_tonnes is highly correlated with arsenicals_tonnes and 11 other fieldsHigh correlation
sulfonamides__including_trimethoprim_tonnes is highly correlated with number_of_countries and 16 other fieldsHigh correlation
tetracyclines_tonnes is highly correlated with number_of_countries and 19 other fieldsHigh correlation
others_tonnes is highly correlated with number_of_countries and 11 other fieldsHigh correlation
aggregated_class_data_tonnes is highly correlated with cephalosporins__all_generations_tonnes and 11 other fieldsHigh correlation
total_antimicrobials_tonnes is highly correlated with aminoglycosides_tonnes and 21 other fieldsHigh correlation
scope is highly correlated with sulfonamides__including_trimethoprim_tonnes and 1 other fieldsHigh correlation
region is highly correlated with 3_4_gen_cephalosporins_tonnes and 4 other fieldsHigh correlation
number_of_countries is highly correlated with aminoglycosides_tonnes and 21 other fieldsHigh correlation
aminoglycosides_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
amphenicols_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
arsenicals_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
cephalosporins__all_generations_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
1_2_gen__cephalosporins_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
3_4_gen_cephalosporins_tonnes is highly correlated with region and 22 other fieldsHigh correlation
fluoroquinolones_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
glycopeptides_tonnes is highly correlated with region and 16 other fieldsHigh correlation
glycophospholipids_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
lincosamides_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
macrolides_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
nitrofurans_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
orthosomycins_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
other_quinolones_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
penicillins_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
pleuromutilins_tonnes is highly correlated with number_of_countries and 21 other fieldsHigh correlation
polypeptides_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
quinoxalines_tonnes is highly correlated with region and 14 other fieldsHigh correlation
streptogramins_tonnes is highly correlated with region and 14 other fieldsHigh correlation
sulfonamides__including_trimethoprim_tonnes is highly correlated with scope and 24 other fieldsHigh correlation
tetracyclines_tonnes is highly correlated with scope and 23 other fieldsHigh correlation
others_tonnes is highly correlated with number_of_countries and 23 other fieldsHigh correlation
aggregated_class_data_tonnes is highly correlated with number_of_countries and 22 other fieldsHigh correlation
total_antimicrobials_tonnes is highly correlated with region and 24 other fieldsHigh correlation
aminoglycosides_tonnes has 2 (11.1%) missing values Missing
amphenicols_tonnes has 2 (11.1%) missing values Missing
arsenicals_tonnes has 2 (11.1%) missing values Missing
cephalosporins__all_generations_tonnes has 2 (11.1%) missing values Missing
1_2_gen__cephalosporins_tonnes has 2 (11.1%) missing values Missing
3_4_gen_cephalosporins_tonnes has 2 (11.1%) missing values Missing
fluoroquinolones_tonnes has 2 (11.1%) missing values Missing
glycopeptides_tonnes has 2 (11.1%) missing values Missing
glycophospholipids_tonnes has 2 (11.1%) missing values Missing
lincosamides_tonnes has 2 (11.1%) missing values Missing
macrolides_tonnes has 2 (11.1%) missing values Missing
nitrofurans_tonnes has 2 (11.1%) missing values Missing
orthosomycins_tonnes has 2 (11.1%) missing values Missing
other_quinolones_tonnes has 2 (11.1%) missing values Missing
penicillins_tonnes has 2 (11.1%) missing values Missing
pleuromutilins_tonnes has 2 (11.1%) missing values Missing
polypeptides_tonnes has 2 (11.1%) missing values Missing
quinoxalines_tonnes has 2 (11.1%) missing values Missing
streptogramins_tonnes has 2 (11.1%) missing values Missing
sulfonamides__including_trimethoprim_tonnes has 2 (11.1%) missing values Missing
tetracyclines_tonnes has 2 (11.1%) missing values Missing
others_tonnes has 2 (11.1%) missing values Missing
aggregated_class_data_tonnes has 2 (11.1%) missing values Missing
total_antimicrobials_tonnes has 12 (66.7%) missing values Missing
scope is uniformly distributed Uniform
region is uniformly distributed Uniform
number_of_countries has 2 (11.1%) zeros Zeros
aminoglycosides_tonnes has 2 (11.1%) zeros Zeros
amphenicols_tonnes has 4 (22.2%) zeros Zeros
cephalosporins__all_generations_tonnes has 5 (27.8%) zeros Zeros
1_2_gen__cephalosporins_tonnes has 6 (33.3%) zeros Zeros
3_4_gen_cephalosporins_tonnes has 6 (33.3%) zeros Zeros
fluoroquinolones_tonnes has 4 (22.2%) zeros Zeros
glycopeptides_tonnes has 10 (55.6%) zeros Zeros
glycophospholipids_tonnes has 6 (33.3%) zeros Zeros
lincosamides_tonnes has 2 (11.1%) zeros Zeros
macrolides_tonnes has 1 (5.6%) zeros Zeros
nitrofurans_tonnes has 7 (38.9%) zeros Zeros
orthosomycins_tonnes has 6 (33.3%) zeros Zeros
other_quinolones_tonnes has 6 (33.3%) zeros Zeros
penicillins_tonnes has 1 (5.6%) zeros Zeros
pleuromutilins_tonnes has 4 (22.2%) zeros Zeros
polypeptides_tonnes has 1 (5.6%) zeros Zeros
quinoxalines_tonnes has 8 (44.4%) zeros Zeros
streptogramins_tonnes has 4 (22.2%) zeros Zeros
sulfonamides__including_trimethoprim_tonnes has 1 (5.6%) zeros Zeros
others_tonnes has 3 (16.7%) zeros Zeros
aggregated_class_data_tonnes has 4 (22.2%) zeros Zeros

Reproduction

Analysis started2023-01-25 00:44:49.362614
Analysis finished2023-01-25 00:46:45.042053
Duration1 minute and 55.68 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

scope
Categorical

HIGH CORRELATION
UNIFORM

Distinct3
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size272.0 B
AGP
All
Terrestrial Food Producing

Length

Max length26
Median length3
Mean length10.66666667
Min length3

Characters and Unicode

Total characters192
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAGP
2nd rowAGP
3rd rowAGP
4th rowAGP
5th rowAGP

Common Values

ValueCountFrequency (%)
AGP6
33.3%
All6
33.3%
Terrestrial Food Producing6
33.3%

Length

2023-01-24T16:46:45.148052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-24T16:46:45.437051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
agp6
20.0%
all6
20.0%
terrestrial6
20.0%
food6
20.0%
producing6
20.0%

Most occurring characters

ValueCountFrequency (%)
r24
12.5%
l18
 
9.4%
o18
 
9.4%
A12
 
6.2%
d12
 
6.2%
12
 
6.2%
i12
 
6.2%
e12
 
6.2%
P12
 
6.2%
s6
 
3.1%
Other values (9)54
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter138
71.9%
Uppercase Letter42
 
21.9%
Space Separator12
 
6.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r24
17.4%
l18
13.0%
o18
13.0%
d12
8.7%
i12
8.7%
e12
8.7%
s6
 
4.3%
t6
 
4.3%
a6
 
4.3%
u6
 
4.3%
Other values (3)18
13.0%
Uppercase Letter
ValueCountFrequency (%)
A12
28.6%
P12
28.6%
G6
14.3%
F6
14.3%
T6
14.3%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin180
93.8%
Common12
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
r24
13.3%
l18
 
10.0%
o18
 
10.0%
A12
 
6.7%
d12
 
6.7%
i12
 
6.7%
e12
 
6.7%
P12
 
6.7%
s6
 
3.3%
t6
 
3.3%
Other values (8)48
26.7%
Common
ValueCountFrequency (%)
12
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r24
12.5%
l18
 
9.4%
o18
 
9.4%
A12
 
6.2%
d12
 
6.2%
12
 
6.2%
i12
 
6.2%
e12
 
6.2%
P12
 
6.2%
s6
 
3.1%
Other values (9)54
28.1%

region
Categorical

HIGH CORRELATION
UNIFORM

Distinct6
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size272.0 B
Africa
Americas
Asia, Far East and Oceania
Europe
Global

Length

Max length26
Median length18.5
Mean length10.5
Min length6

Characters and Unicode

Total characters189
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfrica
2nd rowAmericas
3rd rowAsia, Far East and Oceania
4th rowEurope
5th rowGlobal

Common Values

ValueCountFrequency (%)
Africa3
16.7%
Americas3
16.7%
Asia, Far East and Oceania3
16.7%
Europe3
16.7%
Global3
16.7%
Middle East3
16.7%

Length

2023-01-24T16:46:45.588055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-24T16:46:45.975054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
east6
18.2%
africa3
9.1%
americas3
9.1%
asia3
9.1%
far3
9.1%
and3
9.1%
oceania3
9.1%
europe3
9.1%
global3
9.1%
middle3
9.1%

Most occurring characters

ValueCountFrequency (%)
a30
15.9%
15
 
7.9%
i15
 
7.9%
r12
 
6.3%
e12
 
6.3%
s12
 
6.3%
A9
 
4.8%
l9
 
4.8%
d9
 
4.8%
E9
 
4.8%
Other values (14)57
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter141
74.6%
Uppercase Letter30
 
15.9%
Space Separator15
 
7.9%
Other Punctuation3
 
1.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a30
21.3%
i15
10.6%
r12
 
8.5%
e12
 
8.5%
s12
 
8.5%
l9
 
6.4%
d9
 
6.4%
c9
 
6.4%
t6
 
4.3%
n6
 
4.3%
Other values (6)21
14.9%
Uppercase Letter
ValueCountFrequency (%)
A9
30.0%
E9
30.0%
F3
 
10.0%
O3
 
10.0%
G3
 
10.0%
M3
 
10.0%
Space Separator
ValueCountFrequency (%)
15
100.0%
Other Punctuation
ValueCountFrequency (%)
,3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin171
90.5%
Common18
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a30
17.5%
i15
 
8.8%
r12
 
7.0%
e12
 
7.0%
s12
 
7.0%
A9
 
5.3%
l9
 
5.3%
d9
 
5.3%
E9
 
5.3%
c9
 
5.3%
Other values (12)45
26.3%
Common
ValueCountFrequency (%)
15
83.3%
,3
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a30
15.9%
15
 
7.9%
i15
 
7.9%
r12
 
6.3%
e12
 
6.3%
s12
 
6.3%
A9
 
4.8%
l9
 
4.8%
d9
 
4.8%
E9
 
4.8%
Other values (14)57
30.2%

number_of_countries
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.55555556
Minimum0
Maximum109
Zeros2
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:46.203054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median9.5
Q321.25
95-th percentile51.2
Maximum109
Range109
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation25.84847753
Coefficient of variation (CV)1.472381632
Kurtosis9.687000258
Mean17.55555556
Median Absolute Deviation (MAD)8.5
Skewness2.895687328
Sum316
Variance668.1437908
MonotonicityNot monotonic
2023-01-24T16:46:46.359061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12
 
11.1%
62
 
11.1%
02
 
11.1%
51
 
5.6%
121
 
5.6%
241
 
5.6%
191
 
5.6%
221
 
5.6%
411
 
5.6%
1091
 
5.6%
Other values (5)5
27.8%
ValueCountFrequency (%)
02
11.1%
12
11.1%
31
5.6%
51
5.6%
62
11.1%
91
5.6%
101
5.6%
111
5.6%
121
5.6%
191
5.6%
ValueCountFrequency (%)
1091
5.6%
411
5.6%
371
5.6%
241
5.6%
221
5.6%
191
5.6%
121
5.6%
111
5.6%
101
5.6%
91
5.6%

aminoglycosides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct14
Distinct (%)87.5%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean527.5135089
Minimum0
Maximum2753.984977
Zeros2
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:46.723050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.40425
median57.54466776
Q3874.4566402
95-th percentile1787.558315
Maximum2753.984977
Range2753.984977
Interquartile range (IQR)872.0523902

Descriptive statistics

Standard deviation803.0366342
Coefficient of variation (CV)1.522305345
Kurtosis2.728850804
Mean527.5135089
Median Absolute Deviation (MAD)57.54466776
Skewness1.71333611
Sum8440.216143
Variance644867.8359
MonotonicityNot monotonic
2023-01-24T16:46:46.876052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
02
11.1%
0.7072
11.1%
33.651543541
 
5.6%
762.37578261
 
5.6%
1425.6633331
 
5.6%
527.64331771
 
5.6%
2753.9849771
 
5.6%
4.6511
 
5.6%
3.80168551
 
5.6%
166.50740411
 
5.6%
Other values (4)4
22.2%
(Missing)2
11.1%
ValueCountFrequency (%)
02
11.1%
0.7072
11.1%
2.971
5.6%
3.80168551
5.6%
4.6511
5.6%
33.651543541
5.6%
81.437791991
5.6%
166.50740411
5.6%
527.64331771
5.6%
762.37578261
5.6%
ValueCountFrequency (%)
2753.9849771
5.6%
1465.4160941
5.6%
1425.6633331
5.6%
1210.6992131
5.6%
762.37578261
5.6%
527.64331771
5.6%
166.50740411
5.6%
81.437791991
5.6%
33.651543541
5.6%
4.6511
5.6%

amphenicols_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)81.2%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean713.0834324
Minimum0
Maximum3428.306333
Zeros4
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:47.052070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.125
median24.2100874
Q31090.215723
95-th percentile2674.045276
Maximum3428.306333
Range3428.306333
Interquartile range (IQR)1089.090723

Descriptive statistics

Standard deviation1150.98763
Coefficient of variation (CV)1.614099526
Kurtosis0.5754124609
Mean713.0834324
Median Absolute Deviation (MAD)24.2100874
Skewness1.41094852
Sum11409.33492
Variance1324772.525
MonotonicityNot monotonic
2023-01-24T16:46:47.225055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
04
22.2%
20.410307291
 
5.6%
739.7979251
 
5.6%
2422.6249241
 
5.6%
243.87317631
 
5.6%
3428.3063331
 
5.6%
1.61
 
5.6%
9.35481
 
5.6%
96.027343781
 
5.6%
2141.4691151
 
5.6%
Other values (3)3
16.7%
(Missing)2
11.1%
ValueCountFrequency (%)
04
22.2%
1.51
 
5.6%
1.61
 
5.6%
9.35481
 
5.6%
20.410307291
 
5.6%
28.00986751
 
5.6%
96.027343781
 
5.6%
243.87317631
 
5.6%
739.7979251
 
5.6%
2141.4691151
 
5.6%
ValueCountFrequency (%)
3428.3063331
5.6%
2422.6249241
5.6%
2276.3611261
5.6%
2141.4691151
5.6%
739.7979251
5.6%
243.87317631
5.6%
96.027343781
5.6%
28.00986751
5.6%
20.410307291
5.6%
9.35481
5.6%

arsenicals_tonnes
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)31.2%
Missing2
Missing (%)11.1%
Memory size272.0 B
0.0
74.44
51.9
0.011
126.351

Length

Max length7
Median length6
Mean length3.9375
Min length3

Characters and Unicode

Total characters63
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)12.5%

Sample

1st row0.0
2nd row0.0
3rd row74.44
4th row74.44
5th row0.011

Common Values

ValueCountFrequency (%)
0.08
44.4%
74.443
 
16.7%
51.93
 
16.7%
0.0111
 
5.6%
126.3511
 
5.6%
(Missing)2
 
11.1%

Length

2023-01-24T16:46:47.413054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-01-24T16:46:47.693053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.08
50.0%
74.443
 
18.8%
51.93
 
18.8%
0.0111
 
6.2%
126.3511
 
6.2%

Most occurring characters

ValueCountFrequency (%)
018
28.6%
.16
25.4%
49
14.3%
17
 
11.1%
54
 
6.3%
73
 
4.8%
93
 
4.8%
21
 
1.6%
61
 
1.6%
31
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number47
74.6%
Other Punctuation16
 
25.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018
38.3%
49
19.1%
17
 
14.9%
54
 
8.5%
73
 
6.4%
93
 
6.4%
21
 
2.1%
61
 
2.1%
31
 
2.1%
Other Punctuation
ValueCountFrequency (%)
.16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common63
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018
28.6%
.16
25.4%
49
14.3%
17
 
11.1%
54
 
6.3%
73
 
4.8%
93
 
4.8%
21
 
1.6%
61
 
1.6%
31
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII63
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018
28.6%
.16
25.4%
49
14.3%
17
 
11.1%
54
 
6.3%
73
 
4.8%
93
 
4.8%
21
 
1.6%
61
 
1.6%
31
 
1.6%

cephalosporins__all_generations_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)75.0%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean118.8953223
Minimum0
Maximum592.7990155
Zeros5
Zeros (%)27.8%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:47.870050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.885007704
Q3163.8546689
95-th percentile464.4349344
Maximum592.7990155
Range592.7990155
Interquartile range (IQR)163.8546689

Descriptive statistics

Standard deviation190.3604583
Coefficient of variation (CV)1.601076095
Kurtosis1.234324927
Mean118.8953223
Median Absolute Deviation (MAD)5.885007704
Skewness1.536797506
Sum1902.325157
Variance36237.10409
MonotonicityNot monotonic
2023-01-24T16:46:48.029066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
05
27.8%
10.01789961
 
5.6%
116.27410461
 
5.6%
421.64690741
 
5.6%
44.660103951
 
5.6%
592.79901551
 
5.6%
0.21
 
5.6%
0.076141
 
5.6%
49.938945431
 
5.6%
306.59636171
 
5.6%
Other values (2)2
 
11.1%
(Missing)2
 
11.1%
ValueCountFrequency (%)
05
27.8%
0.076141
 
5.6%
0.21
 
5.6%
1.7521158091
 
5.6%
10.01789961
 
5.6%
44.660103951
 
5.6%
49.938945431
 
5.6%
116.27410461
 
5.6%
306.59636171
 
5.6%
358.36356291
 
5.6%
ValueCountFrequency (%)
592.79901551
5.6%
421.64690741
5.6%
358.36356291
5.6%
306.59636171
5.6%
116.27410461
5.6%
49.938945431
5.6%
44.660103951
5.6%
10.01789961
5.6%
1.7521158091
5.6%
0.21
5.6%

1_2_gen__cephalosporins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct11
Distinct (%)68.8%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean21.42551888
Minimum0
Maximum108.6657981
Zeros6
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:48.310055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.1359681
Q331.27541937
95-th percentile74.22021426
Maximum108.6657981
Range108.6657981
Interquartile range (IQR)31.27541937

Descriptive statistics

Standard deviation31.69482835
Coefficient of variation (CV)1.479302719
Kurtosis2.634656785
Mean21.42551888
Median Absolute Deviation (MAD)1.1359681
Skewness1.673261356
Sum342.8083022
Variance1004.562144
MonotonicityNot monotonic
2023-01-24T16:46:48.473055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
06
33.3%
0.026491
 
5.6%
55.612756461
 
5.6%
27.308859371
 
5.6%
25.717692271
 
5.6%
108.66579811
 
5.6%
0.0021
 
5.6%
43.175099361
 
5.6%
17.315807411
 
5.6%
2.24544621
 
5.6%
(Missing)2
 
11.1%
ValueCountFrequency (%)
06
33.3%
0.0021
 
5.6%
0.026491
 
5.6%
2.24544621
 
5.6%
17.315807411
 
5.6%
25.717692271
 
5.6%
27.308859371
 
5.6%
43.175099361
 
5.6%
55.612756461
 
5.6%
62.738352971
 
5.6%
ValueCountFrequency (%)
108.66579811
5.6%
62.738352971
5.6%
55.612756461
5.6%
43.175099361
5.6%
27.308859371
5.6%
25.717692271
5.6%
17.315807411
5.6%
2.24544621
5.6%
0.026491
5.6%
0.0021
5.6%

3_4_gen_cephalosporins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct11
Distinct (%)68.8%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean80.40148458
Minimum0
Maximum346.0419668
Zeros6
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:48.635051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.6067896045
Q391.30728893
95-th percentile313.8145277
Maximum346.0419668
Range346.0419668
Interquartile range (IQR)91.30728893

Descriptive statistics

Standard deviation136.9080034
Coefficient of variation (CV)1.702804421
Kurtosis-0.3121892919
Mean80.40148458
Median Absolute Deviation (MAD)0.6067896045
Skewness1.294954752
Sum1286.423753
Variance18743.8014
MonotonicityNot monotonic
2023-01-24T16:46:48.774092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
06
33.3%
0.08740961
 
5.6%
25.316200491
 
5.6%
303.0720481
 
5.6%
17.566308671
 
5.6%
346.04196681
 
5.6%
0.000341
 
5.6%
6.7628460661
 
5.6%
289.28055421
 
5.6%
1.1261696091
 
5.6%
(Missing)2
 
11.1%
ValueCountFrequency (%)
06
33.3%
0.000341
 
5.6%
0.08740961
 
5.6%
1.1261696091
 
5.6%
6.7628460661
 
5.6%
17.566308671
 
5.6%
25.316200491
 
5.6%
289.28055421
 
5.6%
297.16990991
 
5.6%
303.0720481
 
5.6%
ValueCountFrequency (%)
346.04196681
5.6%
303.0720481
5.6%
297.16990991
5.6%
289.28055421
5.6%
25.316200491
5.6%
17.566308671
5.6%
6.7628460661
5.6%
1.1261696091
5.6%
0.08740961
5.6%
0.000341
5.6%

fluoroquinolones_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)81.2%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean285.1719573
Minimum0
Maximum1607.20853
Zeros4
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:48.939056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.12
median56.58599895
Q3440.0837616
95-th percentile1052.453066
Maximum1607.20853
Range1607.20853
Interquartile range (IQR)439.9637616

Descriptive statistics

Standard deviation446.0833604
Coefficient of variation (CV)1.564260962
Kurtosis4.46020554
Mean285.1719573
Median Absolute Deviation (MAD)56.58599895
Skewness2.050057802
Sum4562.751317
Variance198990.3645
MonotonicityNot monotonic
2023-01-24T16:46:49.088053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
04
22.2%
91.397797911
 
5.6%
428.54548521
 
5.6%
867.53457851
 
5.6%
219.00166831
 
5.6%
1607.208531
 
5.6%
0.7291
 
5.6%
21.77421
 
5.6%
173.17156191
 
5.6%
474.6985911
 
5.6%
Other values (3)3
16.7%
(Missing)2
11.1%
ValueCountFrequency (%)
04
22.2%
0.161
 
5.6%
0.7291
 
5.6%
4.3627758621
 
5.6%
21.77421
 
5.6%
91.397797911
 
5.6%
173.17156191
 
5.6%
219.00166831
 
5.6%
428.54548521
 
5.6%
474.6985911
 
5.6%
ValueCountFrequency (%)
1607.208531
5.6%
867.53457851
5.6%
674.16712881
5.6%
474.6985911
5.6%
428.54548521
5.6%
219.00166831
5.6%
173.17156191
5.6%
91.397797911
5.6%
21.77421
5.6%
4.3627758621
5.6%

glycopeptides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)37.5%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean0.262875
Minimum0
Maximum1.848
Zeros10
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:49.287072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.2775
95-th percentile1.212
Maximum1.848
Range1.848
Interquartile range (IQR)0.2775

Descriptive statistics

Standard deviation0.5040856905
Coefficient of variation (CV)1.91758703
Kurtosis6.567545034
Mean0.262875
Median Absolute Deviation (MAD)0
Skewness2.507821175
Sum4.206
Variance0.2541023833
MonotonicityNot monotonic
2023-01-24T16:46:49.575052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
010
55.6%
0.2552
 
11.1%
11
 
5.6%
0.5031
 
5.6%
1.8481
 
5.6%
0.3451
 
5.6%
(Missing)2
 
11.1%
ValueCountFrequency (%)
010
55.6%
0.2552
 
11.1%
0.3451
 
5.6%
0.5031
 
5.6%
11
 
5.6%
1.8481
 
5.6%
ValueCountFrequency (%)
1.8481
 
5.6%
11
 
5.6%
0.5031
 
5.6%
0.3451
 
5.6%
0.2552
 
11.1%
010
55.6%

glycophospholipids_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct9
Distinct (%)56.2%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean58.14215169
Minimum0
Maximum244.0464068
Zeros6
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:49.801053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24.30460678
Q3110
95-th percentile144.3297068
Maximum244.0464068
Range244.0464068
Interquartile range (IQR)110

Descriptive statistics

Standard deviation70.61943195
Coefficient of variation (CV)1.214599562
Kurtosis1.551941002
Mean58.14215169
Median Absolute Deviation (MAD)24.30460678
Skewness1.221504014
Sum930.2744271
Variance4987.104169
MonotonicityNot monotonic
2023-01-24T16:46:50.123051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
06
33.3%
1103
16.7%
13.907806781
 
5.6%
97.1831
 
5.6%
111.09080681
 
5.6%
34.701406781
 
5.6%
99.2611
 
5.6%
0.0841
 
5.6%
244.04640681
 
5.6%
(Missing)2
 
11.1%
ValueCountFrequency (%)
06
33.3%
0.0841
 
5.6%
13.907806781
 
5.6%
34.701406781
 
5.6%
97.1831
 
5.6%
99.2611
 
5.6%
1103
16.7%
111.09080681
 
5.6%
244.04640681
 
5.6%
ValueCountFrequency (%)
244.04640681
 
5.6%
111.09080681
 
5.6%
1103
16.7%
99.2611
 
5.6%
97.1831
 
5.6%
34.701406781
 
5.6%
13.907806781
 
5.6%
0.0841
 
5.6%
06
33.3%

lincosamides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct15
Distinct (%)93.8%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean238.2052983
Minimum0
Maximum1332.086904
Zeros2
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:50.339052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1725
median22.1260782
Q3407.4911419
95-th percentile818.5056799
Maximum1332.086904
Range1332.086904
Interquartile range (IQR)407.3186419

Descriptive statistics

Standard deviation371.5333579
Coefficient of variation (CV)1.559719118
Kurtosis4.142287027
Mean238.2052983
Median Absolute Deviation (MAD)22.1260782
Skewness1.942956535
Sum3811.284773
Variance138037.036
MonotonicityNot monotonic
2023-01-24T16:46:50.588052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
02
 
11.1%
22.02107821
 
5.6%
0.211
 
5.6%
22.23107821
 
5.6%
1.364531
 
5.6%
379.67399611
 
5.6%
647.31193861
 
5.6%
303.67643931
 
5.6%
1332.0869041
 
5.6%
0.061
 
5.6%
Other values (5)5
27.8%
(Missing)2
 
11.1%
ValueCountFrequency (%)
02
11.1%
0.02891
5.6%
0.061
5.6%
0.211
5.6%
1.364531
5.6%
10.198144941
5.6%
22.02107821
5.6%
22.23107821
5.6%
50.154780391
5.6%
303.67643931
5.6%
ValueCountFrequency (%)
1332.0869041
5.6%
647.31193861
5.6%
551.32440431
5.6%
490.9425791
5.6%
379.67399611
5.6%
303.67643931
5.6%
50.154780391
5.6%
22.23107821
5.6%
22.02107821
5.6%
10.198144941
5.6%

macrolides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct16
Distinct (%)100.0%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean1235.618155
Minimum0
Maximum6105.203733
Zeros1
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:50.861058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.8425
Q153.31211399
median436.6627662
Q31637.053912
95-th percentile4512.116719
Maximum6105.203733
Range6105.203733
Interquartile range (IQR)1583.741798

Descriptive statistics

Standard deviation1736.074104
Coefficient of variation (CV)1.405024762
Kurtosis3.29078209
Mean1235.618155
Median Absolute Deviation (MAD)432.7677662
Skewness1.833609149
Sum19769.89049
Variance3013953.295
MonotonicityNot monotonic
2023-01-24T16:46:51.055052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01
 
5.6%
6105.2037331
 
5.6%
2542.6964581
 
5.6%
70.045609751
 
5.6%
2131.0530081
 
5.6%
307.94853991
 
5.6%
25.85931
 
5.6%
8.011
 
5.6%
565.37699241
 
5.6%
62.463051991
 
5.6%
Other values (6)6
33.3%
(Missing)2
 
11.1%
ValueCountFrequency (%)
01
5.6%
7.791
5.6%
8.011
5.6%
25.85931
5.6%
62.463051991
5.6%
70.045609751
5.6%
78.341480131
5.6%
307.94853991
5.6%
565.37699241
5.6%
1174.5821
5.6%
ValueCountFrequency (%)
6105.2037331
5.6%
3981.0877151
5.6%
2542.6964581
5.6%
2131.0530081
5.6%
1472.3875461
5.6%
1237.0450521
5.6%
1174.5821
5.6%
565.37699241
5.6%
307.94853991
5.6%
78.341480131
5.6%

nitrofurans_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct10
Distinct (%)62.5%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean0.5304343785
Minimum0
Maximum4.104475028
Zeros7
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:51.253056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.0055
Q30.200847632
95-th percentile3.434862782
Maximum4.104475028
Range4.104475028
Interquartile range (IQR)0.200847632

Descriptive statistics

Standard deviation1.237964872
Coefficient of variation (CV)2.33386998
Kurtosis5.438025049
Mean0.5304343785
Median Absolute Deviation (MAD)0.0055
Skewness2.546570212
Sum8.486950056
Variance1.532557025
MonotonicityNot monotonic
2023-01-24T16:46:51.440066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
07
38.9%
0.166661
 
5.6%
0.3034105281
 
5.6%
3.21165871
 
5.6%
0.41274581
 
5.6%
4.1044750281
 
5.6%
0.011
 
5.6%
0.1381
 
5.6%
0.0011
 
5.6%
0.1391
 
5.6%
(Missing)2
 
11.1%
ValueCountFrequency (%)
07
38.9%
0.0011
 
5.6%
0.011
 
5.6%
0.1381
 
5.6%
0.1391
 
5.6%
0.166661
 
5.6%
0.3034105281
 
5.6%
0.41274581
 
5.6%
3.21165871
 
5.6%
4.1044750281
 
5.6%
ValueCountFrequency (%)
4.1044750281
 
5.6%
3.21165871
 
5.6%
0.41274581
 
5.6%
0.3034105281
 
5.6%
0.166661
 
5.6%
0.1391
 
5.6%
0.1381
 
5.6%
0.011
 
5.6%
0.0011
 
5.6%
07
38.9%

orthosomycins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct11
Distinct (%)68.8%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean15.67006191
Minimum0
Maximum73.45474765
Zeros6
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:51.681054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.227611325
Q326.62968691
95-th percentile48.89468691
Maximum73.45474765
Range73.45474765
Interquartile range (IQR)26.62968691

Descriptive statistics

Standard deviation20.60036237
Coefficient of variation (CV)1.314631843
Kurtosis2.951526651
Mean15.67006191
Median Absolute Deviation (MAD)4.227611325
Skewness1.628100144
Sum250.7209906
Variance424.3749296
MonotonicityNot monotonic
2023-01-24T16:46:51.872052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
06
33.3%
3.642222651
 
5.6%
26.1951
 
5.6%
29.837222651
 
5.6%
27.933747651
 
5.6%
40.7081
 
5.6%
4.8131
 
5.6%
73.454747651
 
5.6%
18.7155251
 
5.6%
3.3531
 
5.6%
(Missing)2
 
11.1%
ValueCountFrequency (%)
06
33.3%
3.3531
 
5.6%
3.642222651
 
5.6%
4.8131
 
5.6%
18.7155251
 
5.6%
22.0685251
 
5.6%
26.1951
 
5.6%
27.933747651
 
5.6%
29.837222651
 
5.6%
40.7081
 
5.6%
ValueCountFrequency (%)
73.454747651
5.6%
40.7081
5.6%
29.837222651
5.6%
27.933747651
5.6%
26.1951
5.6%
22.0685251
5.6%
18.7155251
5.6%
4.8131
5.6%
3.642222651
5.6%
3.3531
5.6%

other_quinolones_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct11
Distinct (%)68.8%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean6.951423906
Minimum0
Maximum48.39944125
Zeros6
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:52.010052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.469645
Q37.412967
95-th percentile27.47828481
Maximum48.39944125
Range48.39944125
Interquartile range (IQR)7.412967

Descriptive statistics

Standard deviation12.88733781
Coefficient of variation (CV)1.853913383
Kurtosis7.220683697
Mean6.951423906
Median Absolute Deviation (MAD)0.469645
Skewness2.578901101
Sum111.2227825
Variance166.0834758
MonotonicityNot monotonic
2023-01-24T16:46:52.138054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
06
33.3%
0.839291
 
5.6%
8.0160181
 
5.6%
20.5045661
 
5.6%
18.939567251
 
5.6%
48.399441251
 
5.6%
0.11
 
5.6%
0.069851
 
5.6%
3.93551
 
5.6%
3.20661
 
5.6%
(Missing)2
 
11.1%
ValueCountFrequency (%)
06
33.3%
0.069851
 
5.6%
0.11
 
5.6%
0.839291
 
5.6%
3.20661
 
5.6%
3.93551
 
5.6%
7.211951
 
5.6%
8.0160181
 
5.6%
18.939567251
 
5.6%
20.5045661
 
5.6%
ValueCountFrequency (%)
48.399441251
5.6%
20.5045661
5.6%
18.939567251
5.6%
8.0160181
5.6%
7.211951
5.6%
3.93551
5.6%
3.20661
5.6%
0.839291
5.6%
0.11
5.6%
0.069851
5.6%

penicillins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct16
Distinct (%)100.0%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean1737.647642
Minimum0
Maximum9762.798535
Zeros1
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:52.309055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01875
Q16.60075
median173.2224641
Q32341.94033
95-th percentile6797.682681
Maximum9762.798535
Range9762.798535
Interquartile range (IQR)2335.33958

Descriptive statistics

Standard deviation2799.096347
Coefficient of variation (CV)1.610853823
Kurtosis3.664864619
Mean1737.647642
Median Absolute Deviation (MAD)173.2099641
Skewness1.935579918
Sum27802.36227
Variance7834940.359
MonotonicityNot monotonic
2023-01-24T16:46:52.553056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
01
 
5.6%
9762.7985351
 
5.6%
4131.1835981
 
5.6%
206.96175721
 
5.6%
3496.1875821
 
5.6%
401.54056691
 
5.6%
23.968692351
 
5.6%
4.8811
 
5.6%
1957.1912461
 
5.6%
0.0251
 
5.6%
Other values (6)6
33.3%
(Missing)2
 
11.1%
ValueCountFrequency (%)
01
5.6%
0.0251
5.6%
2.5251
5.6%
4.8811
5.6%
7.1741
5.6%
7.1991
5.6%
23.968692351
5.6%
139.4831711
5.6%
206.96175721
5.6%
401.54056691
5.6%
ValueCountFrequency (%)
9762.7985351
5.6%
5809.310731
5.6%
4131.1835981
5.6%
3496.1875821
5.6%
1957.1912461
5.6%
1851.9323881
5.6%
401.54056691
5.6%
206.96175721
5.6%
139.4831711
5.6%
23.968692351
5.6%

pleuromutilins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)81.2%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean332.6019644
Minimum0
Maximum1729.598751
Zeros4
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:52.732052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.7
median44.18235687
Q3437.3337808
95-th percentile1303.818174
Maximum1729.598751
Range1729.598751
Interquartile range (IQR)434.6337808

Descriptive statistics

Standard deviation515.5147551
Coefficient of variation (CV)1.54994501
Kurtosis2.606410103
Mean332.6019644
Median Absolute Deviation (MAD)44.18235687
Skewness1.774687968
Sum5321.631431
Variance265755.4627
MonotonicityNot monotonic
2023-01-24T16:46:52.893049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
04
22.2%
37.982856871
 
5.6%
12.3991
 
5.6%
50.381856871
 
5.6%
3.61
 
5.6%
369.87764251
 
5.6%
1161.8913151
 
5.6%
194.22979321
 
5.6%
1729.5987511
 
5.6%
225.37439491
 
5.6%
Other values (3)3
16.7%
(Missing)2
11.1%
ValueCountFrequency (%)
04
22.2%
3.61
 
5.6%
12.3991
 
5.6%
15.75851711
 
5.6%
37.982856871
 
5.6%
50.381856871
 
5.6%
194.22979321
 
5.6%
225.37439491
 
5.6%
369.87764251
 
5.6%
639.70219591
 
5.6%
ValueCountFrequency (%)
1729.5987511
5.6%
1161.8913151
5.6%
880.83510791
5.6%
639.70219591
5.6%
369.87764251
5.6%
225.37439491
5.6%
194.22979321
5.6%
50.381856871
5.6%
37.982856871
5.6%
15.75851711
5.6%

polypeptides_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct16
Distinct (%)100.0%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean968.7928245
Minimum0
Maximum5087.956944
Zeros1
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:53.052052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0375
Q118.98231098
median339.9055394
Q31572.817006
95-th percentile3563.976756
Maximum5087.956944
Range5087.956944
Interquartile range (IQR)1553.834695

Descriptive statistics

Standard deviation1427.768594
Coefficient of variation (CV)1.473760496
Kurtosis3.828667427
Mean968.7928245
Median Absolute Deviation (MAD)335.8348333
Skewness1.94626271
Sum15500.68519
Variance2038523.157
MonotonicityNot monotonic
2023-01-24T16:46:53.227051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.1341
 
5.6%
5087.9569441
 
5.6%
645.40630261
 
5.6%
22.640610551
 
5.6%
445.35861041
 
5.6%
169.39966941
 
5.6%
8.0074122471
 
5.6%
0.051
 
5.6%
234.45246841
 
5.6%
502.0873491
 
5.6%
Other values (6)6
33.3%
(Missing)2
 
11.1%
ValueCountFrequency (%)
01
5.6%
0.051
5.6%
0.1341
5.6%
8.0074122471
5.6%
22.640610551
5.6%
50.477093851
5.6%
169.39966941
5.6%
234.45246841
5.6%
445.35861041
5.6%
502.0873491
5.6%
ValueCountFrequency (%)
5087.9569441
5.6%
3055.983361
5.6%
2016.9793491
5.6%
1746.9940221
5.6%
1514.7581
5.6%
645.40630261
5.6%
502.0873491
5.6%
445.35861041
5.6%
234.45246841
5.6%
169.39966941
5.6%

quinoxalines_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct7
Distinct (%)43.8%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean339.2315
Minimum0
Maximum1381.267
Zeros8
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:53.379057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.075
Q3354.14875
95-th percentile1355.059
Maximum1381.267
Range1381.267
Interquartile range (IQR)354.14875

Descriptive statistics

Standard deviation595.8233839
Coefficient of variation (CV)1.756391679
Kurtosis-0.429067892
Mean339.2315
Median Absolute Deviation (MAD)0.075
Skewness1.279008185
Sum5427.704
Variance355005.5048
MonotonicityNot monotonic
2023-01-24T16:46:53.497052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
08
44.4%
1312.2132
 
11.1%
20.3722
 
11.1%
34.7941
 
5.6%
1346.3231
 
5.6%
1381.2671
 
5.6%
0.151
 
5.6%
(Missing)2
 
11.1%
ValueCountFrequency (%)
08
44.4%
0.151
 
5.6%
20.3722
 
11.1%
34.7941
 
5.6%
1312.2132
 
11.1%
1346.3231
 
5.6%
1381.2671
 
5.6%
ValueCountFrequency (%)
1381.2671
 
5.6%
1346.3231
 
5.6%
1312.2132
 
11.1%
34.7941
 
5.6%
20.3722
 
11.1%
0.151
 
5.6%
08
44.4%

streptogramins_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct13
Distinct (%)81.2%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean120.5466441
Minimum0
Maximum489.4000765
Zeros4
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:54.421060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.003
median3.415193265
Q3126.7440574
95-th percentile477.2942691
Maximum489.4000765
Range489.4000765
Interquartile range (IQR)126.7410574

Descriptive statistics

Standard deviation211.0035213
Coefficient of variation (CV)1.750389012
Kurtosis-0.4309626369
Mean120.5466441
Median Absolute Deviation (MAD)3.415193265
Skewness1.278619868
Sum1928.746306
Variance44522.48602
MonotonicityNot monotonic
2023-01-24T16:46:54.662050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
04
22.2%
4.862386531
 
5.6%
464.7751
 
5.6%
469.63738651
 
5.6%
0.0041
 
5.6%
14.067076531
 
5.6%
473.2591
 
5.6%
1.9681
 
5.6%
489.40007651
 
5.6%
0.1021
 
5.6%
Other values (3)3
16.7%
(Missing)2
11.1%
ValueCountFrequency (%)
04
22.2%
0.0041
 
5.6%
0.1021
 
5.6%
0.2721
 
5.6%
1.9681
 
5.6%
4.862386531
 
5.6%
5.063691
 
5.6%
5.335691
 
5.6%
14.067076531
 
5.6%
464.7751
 
5.6%
ValueCountFrequency (%)
489.40007651
5.6%
473.2591
5.6%
469.63738651
5.6%
464.7751
5.6%
14.067076531
5.6%
5.335691
5.6%
5.063691
5.6%
4.862386531
5.6%
1.9681
5.6%
0.2721
5.6%

sulfonamides__including_trimethoprim_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct16
Distinct (%)100.0%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean659.0270899
Minimum0
Maximum3535.46887
Zeros1
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:54.808052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5775
Q13.4585
median130.8206039
Q3939.2614682
95-th percentile2302.951252
Maximum3535.46887
Range3535.46887
Interquartile range (IQR)935.8029682

Descriptive statistics

Standard deviation1008.305397
Coefficient of variation (CV)1.529990819
Kurtosis3.407854777
Mean659.0270899
Median Absolute Deviation (MAD)129.8731039
Skewness1.851615599
Sum10544.43344
Variance1016679.774
MonotonicityNot monotonic
2023-01-24T16:46:54.940050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3.8631
 
5.6%
3535.468871
 
5.6%
1732.1148491
 
5.6%
165.72870111
 
5.6%
1367.8022371
 
5.6%
158.21083041
 
5.6%
39.248080511
 
5.6%
2.2451
 
5.6%
741.26690181
 
5.6%
01
 
5.6%
Other values (6)6
33.3%
(Missing)2
 
11.1%
ValueCountFrequency (%)
01
5.6%
0.771
5.6%
1.1251
5.6%
2.2451
5.6%
3.8631
5.6%
4.6331
5.6%
39.248080511
5.6%
103.43037751
5.6%
158.21083041
5.6%
165.72870111
5.6%
ValueCountFrequency (%)
3535.468871
5.6%
1892.1120461
5.6%
1732.1148491
5.6%
1367.8022371
5.6%
796.41454531
5.6%
741.26690181
5.6%
165.72870111
5.6%
158.21083041
5.6%
103.43037751
5.6%
39.248080511
5.6%

tetracyclines_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct16
Distinct (%)100.0%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean5530.539968
Minimum3.902
Maximum28151.98723
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:55.097053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.902
5-th percentile4.585009175
Q177.18482762
median1530.458544
Q39459.436856
95-th percentile19459.93731
Maximum28151.98723
Range28148.08523
Interquartile range (IQR)9382.252029

Descriptive statistics

Standard deviation8085.311837
Coefficient of variation (CV)1.461938958
Kurtosis2.995263137
Mean5530.539968
Median Absolute Deviation (MAD)1524.040705
Skewness1.772729241
Sum88488.63949
Variance65372267.5
MonotonicityNot monotonic
2023-01-24T16:46:55.283053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3.9021
 
5.6%
28151.987231
 
5.6%
3798.9488361
 
5.6%
365.09917541
 
5.6%
2778.4762791
 
5.6%
548.03394441
 
5.6%
99.316436831
 
5.6%
10.791
 
5.6%
2320.5690421
 
5.6%
4.81267891
 
5.6%
Other values (6)6
33.3%
(Missing)2
 
11.1%
ValueCountFrequency (%)
3.9021
5.6%
4.81267891
5.6%
8.0231
5.6%
10.791
5.6%
99.316436831
5.6%
365.09917541
5.6%
548.03394441
5.6%
740.34804671
5.6%
2320.5690421
5.6%
2778.4762791
5.6%
ValueCountFrequency (%)
28151.987231
5.6%
16562.587341
5.6%
12293.383681
5.6%
12284.6691
5.6%
8517.6928081
5.6%
3798.9488361
5.6%
2778.4762791
5.6%
2320.5690421
5.6%
740.34804671
5.6%
548.03394441
5.6%

others_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct14
Distinct (%)87.5%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean190.615218
Minimum0
Maximum1228.385873
Zeros3
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:55.432052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.58749555
median26.05958207
Q3226.2303
95-th percentile826.0410979
Maximum1228.385873
Range1228.385873
Interquartile range (IQR)223.6428044

Descriptive statistics

Standard deviation338.7567022
Coefficient of variation (CV)1.777175536
Kurtosis5.652941685
Mean190.615218
Median Absolute Deviation (MAD)26.05958207
Skewness2.358010849
Sum3049.843487
Variance114756.1033
MonotonicityNot monotonic
2023-01-24T16:46:55.697052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
03
16.7%
19.251
 
5.6%
221.41781
 
5.6%
240.66781
 
5.6%
23.584684151
 
5.6%
444.99476221
 
5.6%
691.92617271
 
5.6%
67.880254371
 
5.6%
1228.3858731
 
5.6%
3.19681
 
5.6%
Other values (4)4
22.2%
(Missing)2
11.1%
ValueCountFrequency (%)
03
16.7%
0.75958221
 
5.6%
3.19681
 
5.6%
19.251
 
5.6%
23.3772081
 
5.6%
23.584684151
 
5.6%
28.534481
 
5.6%
55.86807021
 
5.6%
67.880254371
 
5.6%
221.41781
 
5.6%
ValueCountFrequency (%)
1228.3858731
5.6%
691.92617271
5.6%
444.99476221
5.6%
240.66781
5.6%
221.41781
5.6%
67.880254371
5.6%
55.86807021
5.6%
28.534481
5.6%
23.584684151
5.6%
23.3772081
5.6%

aggregated_class_data_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct12
Distinct (%)75.0%
Missing2
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean246.304368
Minimum0
Maximum1532.366004
Zeros4
Zeros (%)22.2%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:55.871054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.04125
median48.84916
Q3213.094
95-th percentile1390.016679
Maximum1532.366004
Range1532.366004
Interquartile range (IQR)213.05275

Descriptive statistics

Standard deviation474.6103055
Coefficient of variation (CV)1.92692606
Kurtosis4.556652199
Mean246.304368
Median Absolute Deviation (MAD)48.84916
Skewness2.367382862
Sum3940.869888
Variance225254.9421
MonotonicityNot monotonic
2023-01-24T16:46:56.003054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
04
22.2%
2.1122
11.1%
212.5661
 
5.6%
214.6781
 
5.6%
1342.5669041
 
5.6%
177.27291
 
5.6%
10.41421
 
5.6%
1532.3660041
 
5.6%
87.284121
 
5.6%
136.051821
 
5.6%
Other values (2)2
11.1%
(Missing)2
11.1%
ValueCountFrequency (%)
04
22.2%
0.0551
 
5.6%
2.1122
11.1%
10.41421
 
5.6%
87.284121
 
5.6%
136.051821
 
5.6%
177.27291
 
5.6%
212.5661
 
5.6%
214.6781
 
5.6%
223.390941
 
5.6%
ValueCountFrequency (%)
1532.3660041
5.6%
1342.5669041
5.6%
223.390941
5.6%
214.6781
5.6%
212.5661
5.6%
177.27291
5.6%
136.051821
5.6%
87.284121
5.6%
10.41421
5.6%
2.1122
11.1%

total_antimicrobials_tonnes
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)100.0%
Missing12
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean23150.84328
Minimum33.924
Maximum69452.52985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size272.0 B
2023-01-24T16:46:56.214053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum33.924
5-th percentile377.9874704
Q12922.04739
median13299.44924
Q335842.45775
95-th percentile62441.77976
Maximum69452.52985
Range69418.60585
Interquartile range (IQR)32920.41036

Descriptive statistics

Standard deviation27373.80981
Coefficient of variation (CV)1.182410916
Kurtosis0.3288960083
Mean23150.84328
Median Absolute Deviation (MAD)12577.3983
Skewness1.152386366
Sum138905.0597
Variance749325463.5
MonotonicityNot monotonic
2023-01-24T16:46:56.425061image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1410.1778821
 
5.6%
19141.242571
 
5.6%
41409.529481
 
5.6%
7457.6559171
 
5.6%
69452.529851
 
5.6%
33.9241
 
5.6%
(Missing)12
66.7%
ValueCountFrequency (%)
33.9241
5.6%
1410.1778821
5.6%
7457.6559171
5.6%
19141.242571
5.6%
41409.529481
5.6%
69452.529851
5.6%
ValueCountFrequency (%)
69452.529851
5.6%
41409.529481
5.6%
19141.242571
5.6%
7457.6559171
5.6%
1410.1778821
5.6%
33.9241
5.6%

Interactions

2023-01-24T16:46:36.033052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:50.754613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:55.599620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:00.566617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:06.187621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:12.385657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:16.925613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:21.841614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:26.208620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:30.755260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:34.709036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:38.479996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:42.212993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:46.491994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:50.930996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:54.787994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:59.280034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:04.237994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:09.912530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:14.481531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:18.586533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:22.621532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:27.384531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:31.811530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:36.196052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:50.953615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:55.805613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:00.791620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:06.465615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:12.531614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:17.092655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:22.014614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:26.417615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:30.902261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:34.852998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:38.623993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:42.344993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:46.750994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:51.073995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:54.999997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:59.429039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:04.393997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:10.083540image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:14.625530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:18.781542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:22.763572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:27.529533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:31.961529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:36.351053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:51.157619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:56.066614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:01.037625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:06.782629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:12.678615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:17.238615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:22.167620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:26.607657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:31.046260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:35.000039image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:38.773996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:42.512994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:46.926994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:51.219993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:55.291000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:59.579996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:04.852996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:10.300537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:14.773529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:18.968529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:22.929531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:27.766611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:32.113528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:36.525051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:51.406674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:56.343615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:01.311618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:07.024623image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:12.834617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:17.400655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:22.415617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:26.775615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:31.498259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:35.156993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:38.930995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:42.662996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:47.081995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:51.370994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:55.597022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:59.818994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:05.173994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:10.479529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:14.942571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:19.128529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:23.082530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:27.942528image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:32.280531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:36.699099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:51.620615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:56.613613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:01.525618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:07.255618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:13.026661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:17.562617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:22.682616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:27.034617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:31.653257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:35.310996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:39.085037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:42.806992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:47.259026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:51.523993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:55.792000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:00.025001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:05.389012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:10.639533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:15.141531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:19.296534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:23.240530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:28.094531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:32.453057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:36.856052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:51.835617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:56.882616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:01.682616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:07.477617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:13.183613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:17.720656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:22.929616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2023-01-24T16:46:39.814051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:55.030616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:00.184615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:05.483656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:12.035654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:16.278615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:21.492619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:25.844615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:30.411261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:34.386008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:38.149993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:41.872035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:46.081003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:50.592034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:54.359994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:58.944000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:03.877996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:09.580571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:14.132575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:18.193529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:22.247575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:27.059568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:31.493571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:35.659051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:39.985051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:44:55.426612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:00.343654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:05.864628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:12.223657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:16.576616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:21.675620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:26.018613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:30.588263image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:34.549996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:38.314995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:42.035998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:46.264016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:50.760993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:54.611035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:45:59.113994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:04.062003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:09.741529image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:14.302531image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:18.353534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:22.417530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:27.230530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:31.651544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-24T16:46:35.850055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-24T16:46:56.625052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-24T16:46:57.304055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-24T16:46:57.951058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-24T16:46:58.484056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-01-24T16:46:58.660050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-01-24T16:46:40.257059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-24T16:46:42.261104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-24T16:46:43.323050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2023-01-24T16:46:44.700053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

scoperegionnumber_of_countriesaminoglycosides_tonnesamphenicols_tonnesarsenicals_tonnescephalosporins__all_generations_tonnes1_2_gen__cephalosporins_tonnes3_4_gen_cephalosporins_tonnesfluoroquinolones_tonnesglycopeptides_tonnesglycophospholipids_tonneslincosamides_tonnesmacrolides_tonnesnitrofurans_tonnesorthosomycins_tonnesother_quinolones_tonnespenicillins_tonnespleuromutilins_tonnespolypeptides_tonnesquinoxalines_tonnesstreptogramins_tonnessulfonamides__including_trimethoprim_tonnestetracyclines_tonnesothers_tonnesaggregated_class_data_tonnestotal_antimicrobials_tonnes
0AGPAfrica1.00.0000000.0000000.0000.0000000.0000000.0000000.0000000.0000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.1340000.0000.0000003.8630003.90200019.2500002.112000NaN
1AGPAmericas5.00.0000000.0000000.0000.0000000.0000000.0000000.0000000.00013.90780722.02107862.4630520.0000003.6422230.0000000.02500037.982857502.0873490.0004.8623870.0000004.812679221.417800212.566000NaN
2AGPAsia, Far East and Oceania6.00.7070000.00000074.4400.0000000.0000000.0000000.0000000.00097.1830000.2100001174.5820000.00000026.1950000.0000007.17400012.3990001514.7580001312.213464.7750000.77000012284.6690000.0000000.000000NaN
3AGPEurope0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4AGPGlobal12.00.7070000.00000074.4400.0000000.0000000.0000000.0000000.000111.09080722.2310781237.0450520.00000029.8372230.0000007.19900050.3818572016.9793491312.213469.6373874.63300012293.383679240.667800214.678000NaN
5AGPMiddle East0.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6AllAfrica24.033.65154420.4103070.01110.0179000.0264900.08741091.3977981.000110.0000001.36453078.3414800.1666600.0000000.839290139.4831713.60000050.4770940.0000.004000103.430377740.34804723.5846842.1120001410.177882
7AllAmericas19.0762.375783739.79792551.900116.27410555.61275625.316200428.5454850.00034.701407379.6739961472.3875460.30341127.9337488.0160181851.932388369.8776421746.99402234.79414.067077796.4145458517.692808444.9947621342.56690419141.242572
8AllAsia, Far East and Oceania22.01425.6633332422.62492474.440421.64690727.308859303.072048867.5345780.00099.261000647.3119393981.0877153.21165940.70800020.5045665809.3107301161.8913153055.9833601346.323473.2590001892.11204616562.587336691.926173177.27290041409.529480
9AllEurope41.0527.643318243.8731760.00044.66010425.71769217.566309219.0016680.5030.084000303.676439565.3769920.4127464.81300018.9395671957.191246194.229793234.4524680.0001.968000741.2669022320.56904267.88025410.4142007457.655917

Last rows

scoperegionnumber_of_countriesaminoglycosides_tonnesamphenicols_tonnesarsenicals_tonnescephalosporins__all_generations_tonnes1_2_gen__cephalosporins_tonnes3_4_gen_cephalosporins_tonnesfluoroquinolones_tonnesglycopeptides_tonnesglycophospholipids_tonneslincosamides_tonnesmacrolides_tonnesnitrofurans_tonnesorthosomycins_tonnesother_quinolones_tonnespenicillins_tonnespleuromutilins_tonnespolypeptides_tonnesquinoxalines_tonnesstreptogramins_tonnessulfonamides__including_trimethoprim_tonnestetracyclines_tonnesothers_tonnesaggregated_class_data_tonnestotal_antimicrobials_tonnes
8AllAsia, Far East and Oceania22.01425.6633332422.62492474.440421.64690727.308859303.072048867.5345780.00099.261000647.3119393981.0877153.21165940.70800020.5045665809.3107301161.8913153055.9833601346.323473.2590001892.11204616562.587336691.926173177.27290041409.529480
9AllEurope41.0527.643318243.8731760.00044.66010425.71769217.566309219.0016680.5030.084000303.676439565.3769920.4127464.81300018.9395671957.191246194.229793234.4524680.0001.968000741.2669022320.56904267.88025410.4142007457.655917
10AllGlobal109.02753.9849773428.306333126.351592.799016108.665798346.0419671607.2085301.848244.0464071332.0869046105.2037334.10447573.45474848.3994419762.7985351729.5987515087.9569441381.267489.4000773535.46887028151.9872331228.3858731532.36600469452.529850
11AllMiddle East3.04.6510001.6000000.0000.2000000.0000000.0000000.7290000.3450.0000000.0600008.0100000.0100000.0000000.1000004.8810000.0000000.0500000.1500.1020002.24500010.7900000.0000000.00000033.924000
12Terrestrial Food ProducingAfrica6.03.8016869.3548000.0000.0761400.0020000.00034021.7742000.000110.0000000.02890025.8593000.1380000.0000000.06985023.9686920.0000008.0074120.0000.00000039.24808199.3164373.1968000.000000NaN
13Terrestrial Food ProducingAmericas11.0166.50740496.02734451.90049.93894543.1750996.762846173.1715620.0000.00000050.154780307.9485400.00000018.7155250.000000401.540567225.374395169.3996690.0005.063690158.210830548.03394423.37720887.284120NaN
14Terrestrial Food ProducingAsia, Far East and Oceania9.01210.6992132141.4691150.000306.59636217.315807289.280554474.6985910.0000.000000490.9425792131.0530080.0000003.3530003.9355003496.187582639.702196445.35861020.3720.2720001367.8022372778.47627928.534480136.051820NaN
15Terrestrial Food ProducingEurope10.081.43779228.0098670.0001.7521162.2454461.1261704.3627760.0000.00000010.19814570.0456100.0010000.0000003.206600206.96175715.75851722.6406110.0000.000000165.728701365.0991750.7595820.055000NaN
16Terrestrial Food ProducingGlobal37.01465.4160942276.36112651.900358.36356362.738353297.169910674.1671290.255110.000000551.3244042542.6964580.13900022.0685257.2119504131.183598880.835108645.40630320.3725.3356901732.1148493798.94883655.868070223.390940NaN
17Terrestrial Food ProducingMiddle East1.02.9700001.5000000.0000.0000000.0000000.0000000.1600000.2550.0000000.0000007.7900000.0000000.0000000.0000002.5250000.0000000.0000000.0000.0000001.1250008.0230000.0000000.000000NaN